Discrete Gauss Transfrom (DGT) commonly appears in areas such as artificial learning, informatics, physics and economy. Due to its inefficiency in terms of speed, especially when we start considering larger problems, faster approximative methods such as Improved Fast Gauss Transform (IFGT) are frequently used instead.
This thesis discusses the implementation of IFGT on the OpenCL platform, which enables us to use processing capabilities of the GPU to accelerate the computation. The implementation is tested using different sets of test data both with and without the graphics card. We compare it with the implementation of DGT, which is also implemented on the CPU and on the GPU using OpenCL. We test our implementation in the computation of continuous Rényi's entropy.
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